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Complexity-entropy analysis at different levels of organisation in written language

Written language is complex. A written text can be considered an attempt to convey a meaningful message which ends up being constrained by language rules, context dependence and highly redundant in its use of resources. Despite all these constraints, unpredictability is an essential element of natur...

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Detalles Bibliográficos
Autores principales: Estevez-Rams, Ernesto, Mesa-Rodriguez, Ania, Estevez-Moya, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505741/
https://www.ncbi.nlm.nih.gov/pubmed/31067221
http://dx.doi.org/10.1371/journal.pone.0214863
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author Estevez-Rams, Ernesto
Mesa-Rodriguez, Ania
Estevez-Moya, Daniel
author_facet Estevez-Rams, Ernesto
Mesa-Rodriguez, Ania
Estevez-Moya, Daniel
author_sort Estevez-Rams, Ernesto
collection PubMed
description Written language is complex. A written text can be considered an attempt to convey a meaningful message which ends up being constrained by language rules, context dependence and highly redundant in its use of resources. Despite all these constraints, unpredictability is an essential element of natural language. Here we present the use of entropic measures to assert the balance between predictability and surprise in written text. In short, it is possible to measure innovation and context preservation in a document. It is shown that this can also be done at the different levels of organization of a text. The type of analysis presented is reasonably general, and can also be used to analyze the same balance in other complex messages such as DNA, where a hierarchy of organizational levels are known to exist.
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spelling pubmed-65057412019-05-23 Complexity-entropy analysis at different levels of organisation in written language Estevez-Rams, Ernesto Mesa-Rodriguez, Ania Estevez-Moya, Daniel PLoS One Research Article Written language is complex. A written text can be considered an attempt to convey a meaningful message which ends up being constrained by language rules, context dependence and highly redundant in its use of resources. Despite all these constraints, unpredictability is an essential element of natural language. Here we present the use of entropic measures to assert the balance between predictability and surprise in written text. In short, it is possible to measure innovation and context preservation in a document. It is shown that this can also be done at the different levels of organization of a text. The type of analysis presented is reasonably general, and can also be used to analyze the same balance in other complex messages such as DNA, where a hierarchy of organizational levels are known to exist. Public Library of Science 2019-05-08 /pmc/articles/PMC6505741/ /pubmed/31067221 http://dx.doi.org/10.1371/journal.pone.0214863 Text en © 2019 Estevez-Rams et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Estevez-Rams, Ernesto
Mesa-Rodriguez, Ania
Estevez-Moya, Daniel
Complexity-entropy analysis at different levels of organisation in written language
title Complexity-entropy analysis at different levels of organisation in written language
title_full Complexity-entropy analysis at different levels of organisation in written language
title_fullStr Complexity-entropy analysis at different levels of organisation in written language
title_full_unstemmed Complexity-entropy analysis at different levels of organisation in written language
title_short Complexity-entropy analysis at different levels of organisation in written language
title_sort complexity-entropy analysis at different levels of organisation in written language
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505741/
https://www.ncbi.nlm.nih.gov/pubmed/31067221
http://dx.doi.org/10.1371/journal.pone.0214863
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